areas are still an unsolved problem for our DSM to DEM transformation. It is a challenge to distinguish a tree from a
house. There are cases when a tree is classified as a house.
Figure 6. Buildings, houses, and trees at 1:1 resolution. AFE can extract buildings, especially flat buildings with
parallel or perpendicular sides, very accurately Figure 7. In urban areas with flat terrain, AFE can perform much better than
in mountainous areas. We recommend that users separate flat urban areas from mountainous areas when running AFE such
that different parameters and strategies can be used.
Figure 7. Extraction of buildings in flat areas
Figure 8. Complex buildings with irregular sides. Complex buildings with irregular sides that are not parallel or
perpendicular to each other still a challenge for AFE. In the AFE GUI, there is an option ―Enforce Building Squaring.‖
When the vast majority of the buildings and houses have parallel and perpendicular sides, users should turn this option
on. The consequence is that the non-parallel sides may not be extracted correctly as shown in Figure 8.
3.3 Campus of the University of Southern California
This is a LIDAR project provided by USC’s Integrated Media Systems Center IMSC with an average post spacing of 0.4
meters for the USC campus Figure 9. . There is no LIDAR data in the lower right corner area, which is either black or
uniformly red. The LIDAR point clouds were converted into a SOCET GXP internal grid format with a post spacing of 0.18
meters. There is a total of 138 million posts. The USC campus is rather flat, but there are many trees surrounding buildings. It
is difficult when surrounding trees have similar heights to the building height.
Figure 9. TSR of USC campus covers 24.8 square kilometers AFE extracted 2464 buildingshouses and 5164 trees Figure
10 in 1 hour 12 minutes with 4 CPUs at 3 GHz each. The time does not include transforming the DSM into a DEM. We used
the following parameters for AFE: minimum building height 2 meters; maximum building width 300 meters; minimum
building width 3 meters; roof detail: 0.4 meters; enforce building squaring on.
The area is relatively flat and the transformation from DSM to DEM is easier than the Allegheny County area. There are many
trees in the center that are difficult to separate from buildings because they overhang the buildings or have similar heights to
the buildings and are attached to them. AFE separated these trees reasonably well from the buildings.
AFE cannot extract buildings such as the football stadium and the track field Figure 11, which are difficult for the DSM to
DEM transformation. They do not have sides that are parallel or perpendicular to each other.
ISPRS Hannover 2011 Workshop, 14-17 June 2011, Hannover, Germany
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Figure 10. Trees are difficult to separate from buildings
Figure 11. AFE cannot extract the football stadium and the track field
Buildings and houses with parallel or perpendicular sides are straightforward for AFE Figure 12, but it may have difficulty
when they less than 3 meters in height. Low 3-D features are difficult for the DSM to DEM transformation.
Figure 12. Rectangular buildings and houses easily extracted.
3.4 Luzern, Switzerland
